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Iterated Amplification

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Iterated Amplification
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2Ben Pace
2jacobjacob

Iterated Amplification is an approach to AI alignment, spearheaded by Paul Christiano. In this setup, we build powerful, aligned ML systems through a process of initially building weak aligned AIs, and recursively using each new AI to build a slightly smarter and still aligned AI. 

See also: Factored cognition. 

Posts tagged Iterated Amplification
4
13Iterated Distillation and Amplification
Ajeya Cotra
5y
7
3
41Paul's research agenda FAQ
Alex Zhu
5y
34
3
42Challenges to Christiano’s capability amplification proposal
Eliezer Yudkowsky
5y
2
3
27A guide to Iterated Amplification & Debate
Rafael Harth
3y
0
2
11AlphaGo Zero and capability amplification
Paul Christiano
4y
16
2
62Debate update: Obfuscated arguments problem
Beth Barnes
2y
14
1
43My Understanding of Paul Christiano's Iterated Amplification AI Safety Research Agenda
Chi Nguyen
3y
9
1
68An overview of 11 proposals for building safe advanced AI
Evan Hubinger
3y
25
1
38My Overview of the AI Alignment Landscape: A Bird's Eye View
Neel Nanda
2y
4
1
48Writeup: Progress on AI Safety via Debate
Beth Barnes, Paul Christiano
3y
15
1
30Relaxed adversarial training for inner alignment
Evan Hubinger
4y
15
1
23Garrabrant and Shah on human modeling in AGI
Rob Bensinger
2y
7
1
21Prize for probable problems
Paul Christiano
5y
0
1
22Corrigibility
Paul Christiano
5y
3
1
16Factored Cognition
Andreas Stuhlmüller
5y
1
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